新常态下货币供给对工业经济的非对称冲击——兼论冲击路径的情景设计与分析

货币政策冲击对工业产出和价格的非对称影响,是新常态下把握好货币供给政策的方向、力度和节奏的重要参考依据,有助于提升货币供给政策的针对性、灵活性和前瞻性。局部投影方法在不同区制下,计算工业产出和价格对货币供给冲击的脉冲响应结果表明：货币供给冲击对工业产出的影响具有不确定性,且总体上表现为中性特征;而货币供给冲击对工业价格的影响不仅在不同区制下,而且在新常态前后均表现出显著的差异性和非对称性,总体来说是短期有效,长期中性的。情景设计的分析结果显示新常态下采用增加货币供给的政策来刺激工业经济是不可取的,其作用效果可能出现工业产出停滞不前,工业价格急剧飙升的工业滞胀。因此,需要从工业产业升级,工业技术创新等工业供给侧寻求工业经济新的增长点和动力机制。     

一种求解双目标flow shop排序问题的进化算法

提出一种求解双目标flow shop排序的递进多目标进化算法.算法采用改进的精英复制策略,在实现精英保留的前提下降低了计算复杂性;通过递进进化模式增加群体多样性,改善了算法收敛性;通过群体进化过程中对非劣解集进行竞争型可变邻域启发式搜索,增强了算法局部搜索性能.采用新算法和参照算法NSGA-II对31个标准双目标flow shop算例进行优化.研究结果表明,新算法在所有算例的求解中均获得了优于NSGA-II的非劣解集,验证了算法的有效性.     

Forest Fire Risk Management and Public Participation in Changing Socioenvironmental Conditions: A Case Study in a Mediterranean Region

Under current conditions of accelerated socioenvironmental change in the Mediterranean forested landscapes, fire is one of the most critical and difficult risks to tackle within the region. This article summarizes the lessons learned from a project based on the participatory integration of qualitative local stakeholders' knowledge with expert GIS fire simulations carried out in the County of El Bages, Catalonia, Spain. First, in this article, a theoretical model--the forest fire circle--is presented in order to explain the reasons for the rise in the damage and frequency of forest fires in this Mediterranean area. Second, it describes the methodology developed and the stages followed during the project. Results show that: (1) the advocacy of old forest reactive management paradigm assumptions and practices based on uncontrolled forest succession can put vast wooded areas of the Mediterranean basin at critical risk; and (2) forest fire management approaches that ignore the crucial role of long-term prevention and local capacity building strategies have failed. In the final section, the content and the specific dimensions of the old reactive paradigm that has characterized forest fire risk management in Catalonia are discussed and contrasted with the possibly emerging preventative paradigm.     

Does Market Incompleteness Matter?

This paper argues that incompleteness of intertemporal financial markets has little effect (on welfare, prices, or consumption) in an economy with a single consumption good, provided that traders are long–lived and patient, a riskless bond is traded, shocks are transitory, and there is no aggregate risk. In an economy with aggregate risk, a similar conclusion holds, provided traders share the same CRRA utility function and the right assets are traded. Examples demonstrate that these conclusions need not hold if the wrong assets are traded or if the economy has multiple consumption goods.     

Flexible parametric estimation technique for a competing risks model with unobserved heterogeneity: a Monte Carlo study

We analyse a flexible parametric estimation technique for a competing risks (CR) model with unobserved heterogeneity, by extending a local mixed proportional hazard single risk model for continuous duration time to a local mixture CR (LMCR) model for discrete duration time. The state-specific local hazard function for the LMCR model is per definition a valid density function if we have either one or two destination states. We conduct Monte Carlo experiments to compare the estimated parameters of the LMCR model, and to compare the estimated parameters of a CR model based on a Heckman–Singer-type (HS-type) technique, with the data-generating process parameters. The Monte Carlo results show that the LMCR model performs better or at least as good as the HS-type model with respect to the estimated structure parameters in most of the cases, but relatively poorer with respect to the estimated duration-dependence parameters.     

BUSDM – an algorithm for the bottom-up search of departures from a model

Searching for regions of the input space where a statistical model is inappropriate is useful in many applications. The study proposes an algorithm for finding local departures from a regression-type prediction model. The algorithm returns low-dimensional hypercubes where the average prediction error clearly departs from zero. The study describes the developed algorithm, and shows successful applications on the simulated and real data from the steel plate production. The algorithms that have been originally developed for searching regions of the high-response value from the input space are reviewed and considered as alternative methods for locating model departures. The proposed algorithm succeeds in locating the model departure regions better than the compared alternatives. The algorithm can be utilized in sequential follow-up of a model as time goes along and new data are observed.     

Diagnostics for elliptical linear mixed models with first-order autoregressive errors

For longitudinal time series data, linear mixed models that contain both random effects across individuals and first-order autoregressive errors within individuals may be appropriate. Some statistical diagnostics based on the models under a proposed elliptical error structure are developed in this work. It is well known that the class of elliptical distributions offers a more flexible framework for modelling since it contains both light- and heavy-tailed distributions. Iterative procedures for the maximum-likelihood estimates of the model parameters are presented. Score tests for the presence of autocorrelation and the homogeneity of autocorrelation coefficients among individuals are constructed. The properties of test statistics are investigated through Monte Carlo simulations. The local influence method for the models is also given. The analysed results of a real data set illustrate the values of the models and diagnostic statistics.     

Piecewise FARIMA models for long-memory time series

We consider the problem of modelling a long-memory time series using piecewise fractional autoregressive integrated moving average processes. The number as well as the locations of structural break points (BPs) and the parameters of each regime are assumed to be unknown. A four-step procedure is proposed to find out the BPs and to estimate the parameters of each regime. Its effectiveness is shown by Monte Carlo simulations and an application to real traffic data modelling is considered.     

Influential observations in GARCH models

This paper examines local influence assessment in generalized autoregressive conditional heteroscesdasticity models with Gaussian and Student-t errors, where influence is examined via the likelihood displacement. The analysis of local influence is discussed under three perturbation schemes: data perturbation, innovative model perturbation and additive model perturbation. For each case, expressions for slope and curvature diagnostics are derived. Monte Carlo experiments are presented to determine the threshold values for locating influential observations. The empirical study of daily returns of the New York Stock Exchange composite index shows that local influence analysis is a useful technique for detecting influential observations; most of the observations detected as influential are associated with historical shocks in the market. Finally, based on this empirical study and the analysis of simulated data, some advice is given on how to use the discussed methodology.     

Local logisitic regression an application to army penetration data

There have been a number of procedures used to analyze non-monotonic binary data to predict the probability of response. Some classical procedures are the Up and Down strategy, the Robbins–Monro procedure, and other sequential optimization designs. Recently, nonparametric procedures such as kernel regression and local linear regression (llogr) have been applied to this type of data. It is a well known fact that kernel regression has problems fitting the data near the boundaries and a drawback with local linear regression is that it may be “too linear” when fitting data from a curvilinear function. The procedure introduced in this paper is called local logistic regression, which fits a logistic regression function at each of the data points. An example is given using United States Army projectile data that supports the use of local logistic regression when analyzing non-monotonic binary data for certain response curves. Properties of local logistic regression will be presented along with simulation results that indicate some of the strengths of the procedure.